An empirical study on robustness of DNNs with out-of-distribution awareness
Publication Type
Conference Proceeding Article
Publication Date
12-2020
Abstract
The state-of-the-art deep neural network (DNN) achieves impressive performance on the input that is similar to training data. However, it fails to make reasonable decisions on the input that is quite different from training data, i.e., out-of-distribution (OOD) examples. Although many techniques have been proposed to detect OOD examples in recent years, it is still a lack of a systematic study about the effectiveness and robustness of different techniques as well as the performance of OOD-aware DNN models. In this paper, we conduct a comprehensive study to unveil the mystery of current OOD detection techniques, and investigate the differences between OOD-unaware/-aware DNNs in model performance, robustness, and uncertainty. We first compare the effectiveness of existing detection techniques and identify the best one. Then, evasion attacks are performed to evaluate the robustness of techniques. Furthermore, we compare the accuracy and robustness between OOD-unaware/-aware DNNs. At last, we study the uncertainty of different models on various kinds of data. Empirical results show OOD-aware detection modules have better performance and are more robust against random noises and evasion attacks. OOD-awareness seldom degrades the accuracy of DNN models in training/test datasets. In contrast, it makes the DNN model more robust against adversarial attacks and noisy inputs. Our study calls for attention to the development of OOD-aware DNN models and the necessity to take data distribution into account when robust and reliable DNN models are desired.
Discipline
OS and Networks | Software Engineering
Research Areas
Software and Cyber-Physical Systems
Publication
Proceedings of the 2020 27th Asia-Pacific Software Engineering Conference (APSEC), Singapore, December 1-4
ISBN
9781728195537
Identifier
10.1109/APSEC51365.2020.00035
Publisher
IEEE
City or Country
Singapore
Citation
ZHOU, Lingjun; YU, Bing; BEREND, David; XIE, Xiaofei; LI, Xiaohong; ZHAO, Jianjun; and LIU, Xusheng.
An empirical study on robustness of DNNs with out-of-distribution awareness. (2020). Proceedings of the 2020 27th Asia-Pacific Software Engineering Conference (APSEC), Singapore, December 1-4.
Available at: https://ink.library.smu.edu.sg/sis_research/7095